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A para-model agent for dynamical systems

Michel, Loïc...

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A para-model agent for dynamical systems by Michel, Loïc ( Author )

Content Provider	Lemonjar Open Access
Publisher	Australian National University
Publication Date	14-07-2023
Summary	Consider a dynamical system $u \mapsto x, \dot{x} = f_{nl}(x,u)$ where $f_{nl}$ is a nonlinear (convex or nonconvex) function, or a combination of nonlinear functions that can eventually switch. We present, in this preliminary work, a generalization of the standard model-free control, that can either control the dynamical system, given an output reference trajectory, or optimize the dynamical system as a derivative-free optimization based "extremum-seeking" procedure. Multiple applications are presented and the robustness of the proposed method is studied in simulation.
ISBN	-
Item Type	Article
File Type	pdf
File Size	29.34 KB
Language	English
Subjects	-
Unlimited	MYR 0.01
Total read	-
URL	http://arxiv.org/abs/1202.4707

Citation

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AMA 11th EditionReferences

Michel, Loïc. A para-model agent for dynamical systems. Australian National University. July 14, 2023. Accessed September 20, 2024. https://online.provdatabase.com/cite/5331

APA 7th EditionReferences

Michel, Loïc. (2023). A para-model agent for dynamical systems. Australian National University. https://online.provdatabase.com/cite/5331

Chicago 17th EditionReferences

Michel, Loïc. A para-model agent for dynamical systems. Australian National University. 2023. https://online.provdatabase.com/cite/5331

HarvardReferences

Michel, Loïc. (2023). A para-model agent for dynamical systems. [Online]. Australian National University. Available from: https://online.provdatabase.com/cite/5331. (Accessed 20 September 2024).

Harvard AustraliaReferences

Michel, Loïc, 2023, A para-model agent for dynamical systems, Australian National University, viewed 20 September 2024, <https://online.provdatabase.com/cite/5331>

MLA 9th EditionWorks Cited

Michel, Loïc. A para-model agent for dynamical systems. Australian National University, 2023. PROV Database, https://online.provdatabase.com/cite/5331.

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